Processing facilities which operate physical processes that process materials, such as manufacturing plants, chemical plants and oil refineries, are typically managed using process control systems. Valves, pumps, motors, heating/cooling devices, and other industrial equipment typically perform actions needed to process the materials in the processing facilities. Among other functions, the process control systems often manage the use of the industrial equipment in the processing facilities.
In conventional process control systems, controllers are often used to control the operation of the industrial equipment in the processing facilities. The controllers can monitor the operation of the industrial equipment, provide control signals to the industrial equipment, and/or generate alarms when malfunctions are detected. Process control systems typically include one or more process controllers and input/output (I/O) devices communicatively coupled to at least one workstation and to one or more field devices, such as through analog and/or digital buses. The field devices can include sensors (e.g., temperature, pressure and flow rate sensors), as well as other passive and/or active devices. The process controllers can receive process information, such as field measurements made by the field devices, in order to implement a control routine. Control signals can then be generated and sent to the industrial equipment to control the operation of the process.
An industrial plant generally has a control room with displays for displaying process parameters such as key temperatures, pressures, fluid flow rates and flow levels, operating positions of key valves, pumps and other equipment, etc. Operators in the control room can control various aspects of the plant operation, typically including overriding automatic control. Generally in a plant operation scenario, the operator desires operating conditions such that the plant always operates at its “optimal” operating point (i.e. where the profit associated with the process is at a maximum, which can correspond to the amount of product generated) and thus close to the alarm limits. Based on changing of the feedstock composition for a chemical process, changing products requirements or economics, or other changes in constraints, the operating conditions may be changed to increase profit. However, there is an increased risk associated with operating the plant closer to the alarm limits due to variability in the process.
Advanced process controllers implement multi-variable Model Predictive Control (MPC) which is an advanced process control (APC) technique for controlling the operation of the equipment running an industrial process. The model is a set of generally linear dynamic relationships between several independent variables and several dependent variables. The model can have different forms, with Laplace transforms and ARX models being conventional model implementations. Non-linear relationships between the variables is also possible.
MPC control techniques typically involve using an empirically derived process model (i.e. based on historical process data) to analyze current input (e.g., sensor) data received, where the model identifies how the industrial equipment should be controlled (e.g., by changing actuator settings) and thus operated based on the input data received. The control principle of MPC uses three (3) types of process variables, manipulated variables MV and some measured disturbance variables (DVs) as the independent variables, and controlled variables (CVs) as the dependent variables. The model includes the response of each CV to MV/DV changes, and the model predicts future effects on the CVs from changes in the MVs and DVs.
In many industrial and commercial customer applications Key Performance Indicators (KPIs) are used by a business KPI monitoring system to track whether a business or organization is performing to acceptable standards, for example in terms of compliance with the law, production rate, energy usage, and maintaining product or service quality, and profitability. Typically there are a wide range of types of KPIs, for example from operator' working time lost due to accidents leading to injuries, maintenance shop performance, environmental emissions through to production rate, quality variations, and energy and chemicals consumption.
Some KPIs used by the business KPI monitoring system are not related to the variables that are within the scope of a MPC controller (e.g., lost time injuries and maintenance shop performance KPIs). However, KPIs relating to production goals, such as feedrate to the process, production rates of various products, product 1 vs. product yield 2, and energy consumption, etc., will typically overlap significantly with the MPC model's CVs, MVs or DVs. In some cases the same variables used to calculate KPIs are also configured as MPC model CVs or MVs (because MPC controls and optimizes the important production variables). In other cases the KPIs will be highly correlated with MPC MVs and DVs, and hence can be predicted/projected using the same MPC tools and workflows. This may include specific energy usage or product yields which can be used for performance monitoring.